class_docstr: Data augmentation method in `InstaBoost: Boosting Instance
  Segmentation Via Probability Map Guided Copy-Pasting
  <https://arxiv.org/abs/1908.07801>`_.
  
  Refer to https://github.com/GothicAi/Instaboost for implementation details.
  
  
  Required Keys:
  
  - img (np.uint8)
  - instances
  
  Modified Keys:
  
  - img (np.uint8)
  - instances
  
  Args:
      action_candidate (tuple): Action candidates. "normal", "horizontal", \
          "vertical", "skip" are supported. Defaults to ('normal', \
          'horizontal', 'skip').
      action_prob (tuple): Corresponding action probabilities. Should be \
          the same length as action_candidate. Defaults to (1, 0, 0).
      scale (tuple): (min scale, max scale). Defaults to (0.8, 1.2).
      dx (int): The maximum x-axis shift will be (instance width) / dx.
          Defaults to 15.
      dy (int): The maximum y-axis shift will be (instance height) / dy.
          Defaults to 15.
      theta (tuple): (min rotation degree, max rotation degree). \
          Defaults to (-1, 1).
      color_prob (float): Probability of images for color augmentation.
          Defaults to 0.5.
      hflag (bool): Whether to use heatmap guided. Defaults to False.
      aug_ratio (float): Probability of applying this transformation. \
          Defaults to 0.5.
code: def __repr__(self) -> str:
      repr_str = self.__class__.__name__
      repr_str += f"(aug_ratio={self.aug_ratio})"
      return repr_str
  
file_path: mmdetection/mmdet/datasets/transforms/instaboost.py
imports: from typing import Tuple
  import numpy
  from mmcv.transforms import BaseTransform
  from mmdet.registry import TRANSFORMS
label_desc: Use image node_id 5 for calling the InstaBoost obj's underlying member method code with example usage: obj.__repr__() and returns: repr_str